Re: st: What is the effect of centering on marginal effects?

[Nick Winter <njgwinter@gmail.com>]

Indeed.

I also wonder why people speak of "inflated" standard errors.  The 
standard errors are correct when the data are (highly) correlated: they 
are telling you that the data don't contain much information on the 
independent effects of the correlated variables.

I've always enjoyed Goldberger's take on the "problem" of multicolinearity:

"Econometrics texts devote many pages to the problem of 
multicollinearity in multiple regression, but they say little about the 
closely analogous problem of small sample size in estimation a 
univariate mean. Perhaps that imbalance is attributable to the lack of 
an exotic polysyllabic name for 'small sample size'. If so, we can 
remove that impediment by introducing the term micronumerosity."

Goldberger, A. S. (1991). A Course in Econometrics. Harvard University 
Press, Cambridge MA.

Quoted at more length here: 
http://davegiles.blogspot.com/2011/09/micronumerosity.html


On 8/3/2012 8:13 AM, William Hauser wrote:
> Dear all,
>
> I'm fairly confident that mean centering does nothing to resolve
> collinearity.  I believe it does fool some of the diagnostic tools
> though and that's probably why the belief that it somehow solves the
> problem persists.  Mean centering simply shifts the collinearity onto
> the intercept term.  Mean centering adds no new information to the
> model and that's the problem - the data lack the necessary information
> for the model to partial out the effects in a precise and stable
> manner.  Perhaps this is effect is different for interaction terms,
> but I fail to see how that's the case.
>
> Collinearity means the independent effects of the collinear variables
> cannot be precisely estimated.  The point of interaction terms is that
> they be analyzed jointly anyway.  The use of the margins and
> marginsplot commands accomplish this with such ease (and polish) that
> I would heartily recommend their use.
>
> Will
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